evaluate-falsifiability
SkillDev toolsLets your agent test whether a hypothesis can be disproven and spot claims that can't be.
Available today. Use it from your connected AI after setup.
No other account needed.
Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
Then ask your AI: use the evaluate-falsifiability skill
About this skill
Determine what observation would falsify a hypothesis and flag unfalsifiable formulations.
What this skill tells your AI
The instructions your AI receives, as published by yogsoth-ai/de-anthropocentric-research-engine in skills/evaluate-falsifiability/SKILL.md and read by ahel’s review.
Purpose
Evaluate whether a claim or hypothesis exposes observations that could count against it.
Input contract
required: [claim, prediction_set, observation_domain]
optional: [auxiliary_assumptions, measurement_limits]
constraints: [falsifying conditions must be observable within the declared domain]
Procedure
- Translate the claim into testable predictions and boundary conditions.
- Identify observations that would contradict the claim under its assumptions.
- Check whether those observations are measurable and independent of the claim's definition.
- Classify falsifiability and list needed operationalization.
Output contract
produces: [falsifiability_assessment, falsifying_observations, operationalization_gaps, assumption_dependencies]
delta_fields: [findings, hypothesis_updates, uncertainties, open_questions]
Quality gates
- At least one non-vacuous potential counter-observation is explicit for a falsifiable claim.
- Auxiliary assumptions are separated from the core claim.
Failure and counterexamples
Do not call a claim falsifiable merely because it can be criticized rhetorically.
Provenance map
resolved: evaluate-falsifiability
Signals
- GitHub stars
- 501
- Forks
- 41
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Key
evaluate-falsifiability- Source
- github.com/yogsoth-ai/de-anthropocentric-research-engine